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<meta name="description" content="Face detection and alignment in unconstrained environment are challenging due to various poses, illuminations and occlusions. Recent studies show that deep learning approaches can achieve impressive performance on these two tasks. In this paper, we propose a deep cascaded multi-task framework which exploits the inherent correlation between detection and alignment to boost up their performance. In particular, our framework leverages a cascaded architecture with three stages of carefully designed deep convolutional networks to predict face and landmark location in a coarse-to-fine manner. In addition, we propose a new online hard sample mining strategy that further improves the performance in practice. Our method achieves superior accuracy over the state-of-the-art techniques on the challenging FDDB and WIDER FACE benchmarks for face detection, and AFLW benchmark for face alignment, while keeps real time performance.">
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	<h1>Joint Face Detection and Alignment using   Multi-task Cascaded Convolutional Networks</h1>

	<div class="authors">
	  <a href="http://kpzhang93.github.io/">Kaipeng Zhang<sup>1</sup></a>&nbsp;&nbsp;
	  <a href="http://personal.ie.cuhk.edu.hk/~zz013/">Zhanpeng Zhang<sup>2</sup></a>&nbsp;&nbsp;
	  <a href="http://dblp.uni-trier.de/pers/hd/l/Li:Zhifeng">Zhifeng Li<sup>1</sup></a>&nbsp;&nbsp;
	  <a href="http://mmlab.siat.ac.cn/yuqiao/">Yu Qiao<sup>1</sup></a>
	</div>

	<div class="affiliations">
	 <sup>1</sup><a href="http://www.siat.ac.cn/jgsz/kyxt/jcs/yjdy/dmtjc/zxtd/"> Multimedia Research Center, </a>
	  <a href="http://english.siat.cas.cn/">Shenzhen Institutes of Advanced Technology, </a>
	  <a href="http://english.cas.cn/">Chinese Academy of Sciences</a>
	  </br>
	  <sup>2</sup>&nbsp;<a href="http://mmlab.ie.cuhk.edu.hk/">Multimedia Laboratory, </a>
	  <a href="http://www.ie.cuhk.edu.hk/">Department of Information Engineering, </a>
	  <a href="http://www.cuhk.edu.hk">The Chinese University of Hong Kong</a>
	</div>

	<div class="venue">IEEE Signal Processing Letters (<a href="http://signalprocessingsociety.org/publications-resources/ieee-signal-processing-letters/ieee-signal-processing-letters" target="_blank">SPL</a>), vol. 23, no. 10, pp. 1499-1503, 2016</div>
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      <div class="section abstract" style="clear: both;text-align:justify; text-justify:inter-ideograph;">
	<h2>Abstract</h2></br>
	<p>
	Face detection and alignment in unconstrained environment are challenging due to various poses, illuminations and occlusions. Recent studies show that deep learning approaches can achieve impressive performance on these two tasks. In this paper, we propose a deep cascaded multi-task framework which exploits the inherent correlation between detection and alignment to boost up their performance. In particular, our framework leverages a cascaded architecture with three stages of carefully designed deep convolutional networks to predict face and landmark location in a coarse-to-fine manner. In addition, we propose a new online hard sample mining strategy that further improves the performance in practice. Our method achieves superior accuracy over the state-of-the-art techniques on the challenging FDDB and WIDER FACE benchmarks for face detection, and AFLW benchmark for face alignment, while keeps real time performance.
	</p>
      </div>
<div class="section downloads">

	<h2>Results</h2><center></br>
		<center><img src="./paper/examples.png" border="0" width="100%"></center></br>
      	<center><img src="./paper/result.png" border="0" width="100%"></center>
      	
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	<h2>Downloads</h2></br>
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		<a href="./paper/spl.pdf" target="_blank" class="imageLink"><img src="./paper/paper.png"></a><br>
		  Paper<br><a href="./paper/spl.pdf">PDF, 5.1 MB</a>
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		<a href="./code/codes.zip" target="_blank" class="imageLink"><img src="./code/code.png"></a><br>
		  Codes<br><a href="./code/codes.zip" target="_blank">ZIP, 12.1 MB</a>
		 (<a href=" https://github.com/kpzhang93/MTCNN_face_detection_alignment">Github</a>)
		
		
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 <div class="section list">
	<h2>Citation</h2>
	
	<div class="section bibtex">
	  <pre>@ARTICLE{7553523, 
author={K. Zhang and Z. Zhang and Z. Li and Y. Qiao}, 
journal={IEEE Signal Processing Letters}, 
title={Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks}, 
year={2016}, 
volume={23}, 
number={10}, 
pages={1499-1503}, 
keywords={Benchmark testing;Computer architecture;Convolution;Detectors;Face;Face detection;Training;Cascaded convolutional neural network (CNN);face alignment;face detection}, 
doi={10.1109/LSP.2016.2603342}, 
ISSN={1070-9908}, 
month={Oct},}
	</pre>
	  </div>
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     <div class="section contact">
	<h2>License</h2></br>
	
	This code is distributed under <a href="https://kpzhang93.github.io/MTCNN_face_detection_alignment/LICENSE">MIT LICENSE</a></br>
	</div>
	
     <div class="section contact">
	<h2>Contact</h2></br>
	
	
	Yu Qiao<br><a href="mailto:yu.qiao@siat.ac.cn">yu.qiao@siat.ac.cn</a></br>
	Kaipeng Zhang<br><a href="mailto:kpzhang@cmlab.csie.ntu.edu.tw">kpzhang@cmlab.csie.ntu.edu.tw</a></br>
	We look forward your sharing of implementation with better runtime efficiency.
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